Abstract
Firefly algorithm (FA) is a recently proposed swarm intelligence optimization technique, which has shown good performance on many optimization problems. In the standard FA and its most variants, a firefly moves to other brighter fireflies. If the current firefly is brighter than another one, the current one will not be conducted any search. In this paper, we propose a new firefly algorithm (called NFA) to address this issue. In NFA, brighter fireflies can move to other positions based on local search. To verify the performance of NFA, thirteen classical benchmark functions are tested. Experimental results show that our NFA outperforms the standard FA and two other modified FAs.
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Acknowledgement
This work is supported by the Humanity and Social Science Foundation of Ministry of Education of China (No. 13YJCZH174), the National Natural Science Foundation of China (Nos. 61305150 and 61261039), the Science and Technology Plan Project of Jiangxi Provincial Education Department (Nos. GJJ14747 and GJJ13762), and the Natural Science Foundation of Jiangxi Province (No. 20142BAB217020).
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Wang, H. et al. (2016). A New Firefly Algorithm with Local Search for Numerical Optimization. In: Li, K., Li, J., Liu, Y., Castiglione, A. (eds) Computational Intelligence and Intelligent Systems. ISICA 2015. Communications in Computer and Information Science, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-10-0356-1_2
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DOI: https://doi.org/10.1007/978-981-10-0356-1_2
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